Data Governance Lead (VISA INDEPENDENT CANDIDATES ONLY)

About the Role:

Client is the strategic advisor behind some of the most ambitious Data Governance programs in the enterprise. As clients move from AI experimentation into production, the need for structured decision rights around data becomes immediate, and that is where this role sits. The Data Governance Lead will own the people-and-process work that makes the semantic layer operational in a client environment: stewardship models, decision rights frameworks, policy design, and the connective tissue that links data governance accountability to AI/ML outcomes.

This is a high-visibility position embedded with VP and Director level stakeholders. The role is often responsible for leading the FSFP engagement team as well as the project itself. The work is iterative and sustained, not a one-time stand-up.

Key Responsibilities:

• Engagement Leadership: Lead the engagement team and own both the engagement roadmap and engagement deliverables from development to adoption.

• Decision Rights Framework: Define and operationalize the decision rights model that governs how data is approved, consumed, and changed across downstream business processes.

• Stewardship Model Design: Establish the stewardship structure (domain stewards, data owners, governance councils), and codify the responsibilities required to sustain trusted data.

• Policy Design: Author governance policies, standards, and procedures covering data quality, access, retention, AI training data eligibility, and model output accountability.

• Operating Model: Build the day-to-day operating model that connects governance forums, escalation paths, and KPIs to the client's broader data governance roadmap.

• Stakeholder Advisory: Serve as the primary governance advisor to VP- and Director-level stakeholders; translate strategic intent into actionable governance commitments.

• Program Leadership: Lead and facilitate the governance council, working groups, and adoption committees that drive organizational alignment.

• Cross-Workstream Integration: Partner with FSFP workstream leads and the client's metadata, MDM, and AI workstreams to ensure governance decisions reinforce the enterprise data foundation.

• Program Health: Define and report on governance maturity, adoption metrics, and ROI signals tied to client business priorities.

Required Qualifications:

• 8+ years of data governance experience, with at least 3 years leading governance programs in a consulting or enterprise capacity.
• Demonstrated ability to design and implement decision rights frameworks, stewardship models, and policy libraries from the ground up.
• Experience working across multiple data types and domains and the ability to ramp quickly into unfamiliar subject matter.
• Proven ability to advise VP/Director-level stakeholders and lead committees through ambiguity.
• Strong written and verbal communication skills; able to explain governance value to both business and technical audiences.

Preferred Qualifications:

• Experience operationalizing governance in support of AI Governance, Responsible AI, or AI Risk Management programs.
• Familiarity with one or more governance and catalog platforms (tool-agnostic; we look for transferable expertise).
• Industry experience in life sciences, financial services, healthcare, or other regulated environments.
• Experience with industry frameworks such as DAMA-DMBOK, DCAM, or EDM Council CDMC.
• Background in regulated industries (life sciences, financial services, healthcare) where AI governance is under regulatory scrutiny.
• Familiarity with semantic layer concepts (ontologies, business glossaries, metric frameworks) as the foundation that governance enforces.
• CDMP, IQCP, or equivalent credentials.

What Success Looks Like:

The Data Governance Lead s work on a successful engagement may include:

• Align executive sponsors and VP/Director stakeholders around the governance vision and, where appropriate, its connection to AI Governance objectives.
• Complete maturity assessment, stakeholder mapping, and pain-point inventory across priority data domains.
• Deliver a documented decision rights and stewardship model, an approved policy library, and a sequenced roadmap aligned to the client's AI workflows.
• Stand up the governance council and working groups with measurable cadence and decision throughput; embed governance touchpoints into the client's development and deployment lifecycle.

• Align downstream data governance practitioners around a shared governance operating model.